





"""simple docstring"""





import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class 						lowercase   (   _UpperCAmelCase	,    unittest.TestCase   ):
      _SCREAMING_SNAKE_CASE							      = DiTPipeline
      _SCREAMING_SNAKE_CASE							      = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
      _SCREAMING_SNAKE_CASE							      = PipelineTesterMixin.required_optional_params - {
          'latents',
          'num_images_per_prompt',
          'callback',
          'callback_steps',
      }
      _SCREAMING_SNAKE_CASE							      = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
      _SCREAMING_SNAKE_CASE							      = False





      def        _snake_case     (			self					)						->	Any:
            torch.manual_seed(0					)
            lowerCAmelCase    		=			TransformeraDModel(
                sample_size=16 ,		num_layers=2 ,		patch_size=4 ,		attention_head_dim=8 ,		num_attention_heads=2 ,		in_channels=4 ,		out_channels=8 ,		attention_bias=lowercase ,		activation_fn="""gelu-approximate""" ,		num_embeds_ada_norm=1_000 ,		norm_type="""ada_norm_zero""" ,		norm_elementwise_affine=lowercase ,		)
            lowerCAmelCase    		=			AutoencoderKL()
            lowerCAmelCase    		=			DDIMScheduler()
            lowerCAmelCase    		=			{"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
            return components





      def        _snake_case     (			self ,		lowercase ,		lowercase=0					)						->	str:
            if str(lowercase					).startswith("""mps"""					):
                  lowerCAmelCase    		=			torch.manual_seed(lowercase					)
            else:
                  lowerCAmelCase    		=			torch.Generator(device=lowercase					).manual_seed(lowercase					)
            lowerCAmelCase    		=			{
                """class_labels""": [1],
                """generator""": generator,
                """num_inference_steps""": 2,
                """output_type""": """numpy""",
            }
            return inputs





      def        _snake_case     (			self					)						->	Any:
            lowerCAmelCase    		=			"""cpu"""

            lowerCAmelCase    		=			self.get_dummy_components()
            lowerCAmelCase    		=			self.pipeline_class(**lowercase					)
            pipe.to(lowercase					)
            pipe.set_progress_bar_config(disable=lowercase					)

            lowerCAmelCase    		=			self.get_dummy_inputs(lowercase					)
            lowerCAmelCase    		=			pipe(**lowercase					).images
            lowerCAmelCase    		=			image[0, -3:, -3:, -1]

            self.assertEqual(image.shape ,		(1, 16, 16, 3)					)
            lowerCAmelCase    		=			np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457]					)
            lowerCAmelCase    		=			np.abs(image_slice.flatten() - expected_slice					).max()
            self.assertLessEqual(lowercase ,		1e-3					)





      def        _snake_case     (			self					)						->	List[str]:
            self._test_inference_batch_single_identical(relax_max_difference=lowercase ,		expected_max_diff=1e-3					)





      @unittest.skipIf(
          torch_device != """cuda""" or not is_xformers_available() ,		reason="""XFormers attention is only available with CUDA and `xformers` installed""" ,		)
      def        _snake_case     (			self					)						->	int:
            self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3					)







@require_torch_gpu
@slow
class 						lowercase   (   unittest.TestCase   ):





      def        _snake_case     (			self					)						->	str:
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()





      def        _snake_case     (			self					)						->	List[str]:
            lowerCAmelCase    		=			torch.manual_seed(0					)

            lowerCAmelCase    		=			DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256"""					)
            pipe.to("""cuda"""					)

            lowerCAmelCase    		=			["""vase""", """umbrella""", """white shark""", """white wolf"""]
            lowerCAmelCase    		=			pipe.get_label_ids(lowercase					)

            lowerCAmelCase    		=			pipe(lowercase ,		generator=lowercase ,		num_inference_steps=40 ,		output_type="""np"""					).images

            for word, image in zip(lowercase ,		lowercase					):
                  lowerCAmelCase    		=			load_numpy(
                      f'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'					)
                  assert np.abs((expected_image - image).max()					) < 1e-2





      def        _snake_case     (			self					)						->	List[str]:
            lowerCAmelCase    		=			DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""					)
            lowerCAmelCase    		=			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config					)
            pipe.to("""cuda"""					)

            lowerCAmelCase    		=			["""vase""", """umbrella"""]
            lowerCAmelCase    		=			pipe.get_label_ids(lowercase					)

            lowerCAmelCase    		=			torch.manual_seed(0					)
            lowerCAmelCase    		=			pipe(lowercase ,		generator=lowercase ,		num_inference_steps=25 ,		output_type="""np"""					).images

            for word, image in zip(lowercase ,		lowercase					):
                  lowerCAmelCase    		=			load_numpy(
                      """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                      f'/dit/{word}_512.npy'					)

                  assert np.abs((expected_image - image).max()					) < 1e-1













"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__   =  {
    "configuration_roberta_prelayernorm": [
        "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "RobertaPreLayerNormConfig",
        "RobertaPreLayerNormOnnxConfig",
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "RobertaPreLayerNormForCausalLM",
           "RobertaPreLayerNormForMaskedLM",
           "RobertaPreLayerNormForMultipleChoice",
           "RobertaPreLayerNormForQuestionAnswering",
           "RobertaPreLayerNormForSequenceClassification",
           "RobertaPreLayerNormForTokenClassification",
           "RobertaPreLayerNormModel",
           "RobertaPreLayerNormPreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFRobertaPreLayerNormForCausalLM",
           "TFRobertaPreLayerNormForMaskedLM",
           "TFRobertaPreLayerNormForMultipleChoice",
           "TFRobertaPreLayerNormForQuestionAnswering",
           "TFRobertaPreLayerNormForSequenceClassification",
           "TFRobertaPreLayerNormForTokenClassification",
           "TFRobertaPreLayerNormMainLayer",
           "TFRobertaPreLayerNormModel",
           "TFRobertaPreLayerNormPreTrainedModel",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "FlaxRobertaPreLayerNormForCausalLM",
           "FlaxRobertaPreLayerNormForMaskedLM",
           "FlaxRobertaPreLayerNormForMultipleChoice",
           "FlaxRobertaPreLayerNormForQuestionAnswering",
           "FlaxRobertaPreLayerNormForSequenceClassification",
           "FlaxRobertaPreLayerNormForTokenClassification",
           "FlaxRobertaPreLayerNormModel",
           "FlaxRobertaPreLayerNormPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_roberta_prelayernorm import (
           ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
           RobertaPreLayerNormConfig,
           RobertaPreLayerNormOnnxConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roberta_prelayernorm import (
                  ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RobertaPreLayerNormForCausalLM,
                  RobertaPreLayerNormForMaskedLM,
                  RobertaPreLayerNormForMultipleChoice,
                  RobertaPreLayerNormForQuestionAnswering,
                  RobertaPreLayerNormForSequenceClassification,
                  RobertaPreLayerNormForTokenClassification,
                  RobertaPreLayerNormModel,
                  RobertaPreLayerNormPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roberta_prelayernorm import (
                  TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRobertaPreLayerNormForCausalLM,
                  TFRobertaPreLayerNormForMaskedLM,
                  TFRobertaPreLayerNormForMultipleChoice,
                  TFRobertaPreLayerNormForQuestionAnswering,
                  TFRobertaPreLayerNormForSequenceClassification,
                  TFRobertaPreLayerNormForTokenClassification,
                  TFRobertaPreLayerNormMainLayer,
                  TFRobertaPreLayerNormModel,
                  TFRobertaPreLayerNormPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roberta_prelayernorm import (
                  FlaxRobertaPreLayerNormForCausalLM,
                  FlaxRobertaPreLayerNormForMaskedLM,
                  FlaxRobertaPreLayerNormForMultipleChoice,
                  FlaxRobertaPreLayerNormForQuestionAnswering,
                  FlaxRobertaPreLayerNormForSequenceClassification,
                  FlaxRobertaPreLayerNormForTokenClassification,
                  FlaxRobertaPreLayerNormModel,
                  FlaxRobertaPreLayerNormPreTrainedModel,
              )

else:
       import sys

       SCREAMING_SNAKE_CASE__   =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






